Denergium provides EnergyScopium, a software‑only platform that adds fine‑grained energy profiling and real‑time telemetry to existing HPC systems. It captures per‑function power use on CPUs, GPUs and accelerators, offers code‑level optimization recommendations, and uses AI‑driven scheduling to improve workload placement and hardware configuration, integrating with common resource managers without hardware changes.
Funding
Funding not disclosed
Founders
Product
Problem
High-performance computing (HPC) facilities often run at the limits of power budgets, yet lack granular visibility into software‑hardware energy interactions. This makes it difficult to identify code inefficiencies, schedule workloads intelligently, and achieve higher FLOPS per Watt without sacrificing performance.
Solution
Denergium delivers the EnergyScopium suite, a software‑only platform that brings detailed energy profiling, data‑driven infrastructure optimization, and real‑time telemetry to existing HPC stacks. The Profiler captures per‑function power consumption on CPUs, GPUs, and accelerators, then generates actionable code‑level recommendations. Smart Decision applies machine‑learning models to workload scheduling and hardware configuration, producing cost‑benefit analyses that guide resource‑manager decisions. Embedded provides an API and dashboard widgets that expose live energy metrics to monitoring tools and user portals, enabling continuous observability. All components integrate transparently with common HPC resource managers and monitoring stacks, requiring no hardware modifications and preserving existing performance characteristics while boosting computational efficiency.
Target Audience
Primary customers are HPC center operators, system architects, and performance engineers in research institutions, AI training clusters, and large‑scale compute facilities seeking to lower energy costs while increasing computational throughput.
Features
- Fine‑grained energy profiling engine that records power draw per kernel, thread, and memory operation across heterogeneous HPC hardware
- Automated code‑optimization suggestions based on energy‑performance trade‑off analysis and compiler‑aware heuristics
- AI‑driven workload scheduling module that predicts energy impact of job placement and recommends optimal node configurations
- Cost‑benefit analysis dashboard delivering ROI estimates for infrastructure re‑tuning and hardware upgrades
- Real‑time telemetry API compatible with Prometheus, Grafana, and custom dashboards for continuous observability
- Open‑source integration module for Slurm, PBS, and other resource managers, deployable as a loadable environment module
- Zero‑touch deployment model that works with existing software stacks and does not require firmware changes
- Secure, encrypted data pipeline with role‑based access controls for multi‑tenant HPC environments